{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.1.0\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import numpy as np\n",
    "import sklearn\n",
    "import sys\n",
    "import os\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "\n",
    "from tensorflow.python import keras\n",
    "\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 746 images belonging to 2 classes.\n",
      "Found 746 images belonging to 2 classes.\n",
      "746 746\n"
     ]
    }
   ],
   "source": [
    "root_directory=\"../input/ogjdata\"\n",
    "height =256\n",
    "width =256\n",
    "channels = 3\n",
    "batch_size = 64\n",
    "num_classes = 2\n",
    "# 数据增强\n",
    "class LRN(keras.layers.Layer):\n",
    "    def __init__(self):\n",
    "        super(LRN, self).__init__()\n",
    "        self.depth_radius=2\n",
    "        self.bias=1\n",
    "        self.alpha=1e-4\n",
    "        self.beta=0.75\n",
    "    def call(self,x):\n",
    "        return tf.nn.lrn(x,depth_radius=self.depth_radius,\n",
    "                         bias=self.bias,alpha=self.alpha,\n",
    "                         beta=self.beta)\n",
    "train_datagen = keras.preprocessing.image.ImageDataGenerator(\n",
    "    rescale=1./255,\n",
    "    rotation_range =40,       # 旋转40度\n",
    "    width_shift_range = 0.2,  # 位移 0.2 个比例 20% 平移\n",
    "    height_shift_range = 0.2,  #\n",
    "    shear_range = 0.3,         # 增强强度\n",
    "    zoom_range = 0.2,\n",
    "    horizontal_flip = True,\n",
    "    fill_mode = 'nearest',   # 填充\n",
    ")\n",
    "train_generator = train_datagen.flow_from_directory(root_directory,\n",
    "                                                   target_size = (height,width),\n",
    "                                                   batch_size = batch_size,\n",
    "                                                   seed = 7,\n",
    "                                                   shuffle =True,\n",
    "                                                   class_mode = \"sparse\")\n",
    "validation_datagen = keras.preprocessing.image.ImageDataGenerator(\n",
    "    rescale=1./255,\n",
    ")\n",
    "validation_generator = validation_datagen.flow_from_directory(root_directory,\n",
    "                                                   target_size = (height,width),\n",
    "                                                   batch_size = batch_size,\n",
    "                                                   seed = 7,\n",
    "                                                   shuffle =False,\n",
    "                                                   class_mode = \"sparse\")\n",
    "train_num = train_generator.samples\n",
    "valid_num = validation_generator.samples\n",
    "print(train_num,valid_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(64, 256, 256, 3) (64,)\n",
      "[1. 1. 1. 0. 0. 1. 1. 1. 1. 0. 0. 0. 0. 1. 1. 0. 1. 1. 0. 0. 1. 0. 1. 1.\n",
      " 1. 1. 0. 1. 0. 1. 1. 0. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 0. 1. 0. 0. 0. 1.\n",
      " 0. 1. 0. 0. 1. 0. 1. 1. 1. 0. 0. 1. 1. 1. 0. 1.]\n",
      "(64, 256, 256, 3) (64,)\n",
      "[0. 1. 1. 0. 1. 0. 1. 1. 0. 1. 0. 1. 0. 0. 1. 1. 1. 0. 1. 0. 1. 0. 0. 0.\n",
      " 1. 1. 1. 1. 1. 0. 1. 1. 1. 0. 0. 1. 0. 0. 0. 0. 1. 1. 1. 0. 1. 1. 0. 1.\n",
      " 1. 0. 0. 1. 1. 0. 1. 1. 0. 0. 1. 0. 0. 0. 0. 1.]\n"
     ]
    }
   ],
   "source": [
    "for i in range(2):\n",
    "    x,y = train_generator.next()\n",
    "    print(x.shape,y.shape)\n",
    "    print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 64, 64, 96)        34944     \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 31, 31, 96)        0         \n",
      "_________________________________________________________________\n",
      "lrn (LRN)                    (None, 31, 31, 96)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 31, 31, 256)       614656    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 15, 15, 256)       0         \n",
      "_________________________________________________________________\n",
      "lrn_1 (LRN)                  (None, 15, 15, 256)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 15, 15, 384)       885120    \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 15, 15, 384)       1327488   \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 15, 15, 256)       884992    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 7, 7, 256)         0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 12544)             0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 4096)              51384320  \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 4096)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 4096)              16781312  \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 4096)              0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 2)                 8194      \n",
      "=================================================================\n",
      "Total params: 71,921,026\n",
      "Trainable params: 71,921,026\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "#%%\n",
    "model = keras.models.Sequential()\n",
    "model.add(keras.layers.Conv2D(filters=96,\n",
    "                              kernel_size=(11,11),\n",
    "                              strides=4,\n",
    "                              activation='relu',\n",
    "                              padding='same',\n",
    "                              input_shape=[width,height,channels]))\n",
    "model.add(keras.layers.MaxPool2D(pool_size=(3,3),strides=2))\n",
    "model.add(LRN())\n",
    "model.add(keras.layers.Conv2D(filters=256,\n",
    "                              kernel_size=(5,5),\n",
    "                              strides=1,\n",
    "                              activation='relu',\n",
    "                              padding='same'))\n",
    "model.add(keras.layers.MaxPool2D(pool_size=(3,3),strides=2))\n",
    "model.add(LRN())\n",
    "model.add(keras.layers.Conv2D(filters=384,\n",
    "                              kernel_size=(3,3),\n",
    "                              strides=1,\n",
    "                              activation='relu',\n",
    "                              padding='same'))\n",
    "\n",
    "model.add(keras.layers.Conv2D(filters=384,\n",
    "                              kernel_size=(3,3),\n",
    "                              strides=1,\n",
    "                              activation='relu',\n",
    "                              padding='same'))\n",
    "model.add(keras.layers.Conv2D(filters=256,\n",
    "                              kernel_size=(3,3),\n",
    "                              strides=1,\n",
    "                              activation='relu',\n",
    "                              padding='same'))\n",
    "model.add(keras.layers.MaxPool2D(pool_size=(3,3),strides=2))\n",
    "\n",
    "model.add(keras.layers.Flatten())\n",
    "model.add(keras.layers.Dense(4096,activation='relu'))\n",
    "model.add(keras.layers.Dropout(0.5))\n",
    "model.add(keras.layers.Dense(4096,activation='relu'))\n",
    "model.add(keras.layers.Dropout(0.5))\n",
    "model.add(keras.layers.Dense(num_classes,activation=\"softmax\"))\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train for 11 steps, validate for 11 steps\n",
      "Epoch 1/300\n",
      "11/11 [==============================] - 23s 2s/step - loss: 0.6926 - accuracy: 0.5367 - val_loss: 0.6937 - val_accuracy: 0.5043\n",
      "Epoch 2/300\n",
      "11/11 [==============================] - 20s 2s/step - loss: 0.6938 - accuracy: 0.5469 - val_loss: 0.6928 - val_accuracy: 0.5043\n",
      "Epoch 3/300\n",
      "11/11 [==============================] - 20s 2s/step - loss: 0.6942 - accuracy: 0.5249 - val_loss: 0.6928 - val_accuracy: 0.5043\n",
      "Epoch 4/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6937 - accuracy: 0.5191 - val_loss: 0.6920 - val_accuracy: 0.5369\n",
      "Epoch 5/300\n",
      "11/11 [==============================] - 20s 2s/step - loss: 0.6920 - accuracy: 0.5279 - val_loss: 0.6915 - val_accuracy: 0.5923\n",
      "Epoch 6/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6918 - accuracy: 0.5235 - val_loss: 0.6915 - val_accuracy: 0.5043\n",
      "Epoch 7/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6919 - accuracy: 0.5176 - val_loss: 0.6912 - val_accuracy: 0.5043\n",
      "Epoch 8/300\n",
      "11/11 [==============================] - 20s 2s/step - loss: 0.6936 - accuracy: 0.5059 - val_loss: 0.6903 - val_accuracy: 0.5753\n",
      "Epoch 9/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6920 - accuracy: 0.5191 - val_loss: 0.6901 - val_accuracy: 0.6293\n",
      "Epoch 10/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6914 - accuracy: 0.5220 - val_loss: 0.6900 - val_accuracy: 0.6548\n",
      "Epoch 11/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6900 - accuracy: 0.5601 - val_loss: 0.6898 - val_accuracy: 0.5170\n",
      "Epoch 12/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6904 - accuracy: 0.5337 - val_loss: 0.6892 - val_accuracy: 0.6491\n",
      "Epoch 13/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6899 - accuracy: 0.5528 - val_loss: 0.6889 - val_accuracy: 0.6477\n",
      "Epoch 14/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6898 - accuracy: 0.5352 - val_loss: 0.6888 - val_accuracy: 0.5511\n",
      "Epoch 15/300\n",
      "11/11 [==============================] - 20s 2s/step - loss: 0.6888 - accuracy: 0.5455 - val_loss: 0.6892 - val_accuracy: 0.5043\n",
      "Epoch 16/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6884 - accuracy: 0.5572 - val_loss: 0.6879 - val_accuracy: 0.5639\n",
      "Epoch 17/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6877 - accuracy: 0.5308 - val_loss: 0.6869 - val_accuracy: 0.6023\n",
      "Epoch 18/300\n",
      "11/11 [==============================] - 20s 2s/step - loss: 0.6880 - accuracy: 0.5543 - val_loss: 0.6864 - val_accuracy: 0.5710\n",
      "Epoch 19/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6840 - accuracy: 0.5880 - val_loss: 0.6859 - val_accuracy: 0.6477\n",
      "Epoch 20/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6866 - accuracy: 0.5762 - val_loss: 0.6851 - val_accuracy: 0.5511\n",
      "Epoch 21/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6850 - accuracy: 0.5704 - val_loss: 0.6852 - val_accuracy: 0.5938\n",
      "Epoch 22/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6864 - accuracy: 0.5689 - val_loss: 0.6843 - val_accuracy: 0.5866\n",
      "Epoch 23/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6810 - accuracy: 0.5748 - val_loss: 0.6835 - val_accuracy: 0.6392\n",
      "Epoch 24/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6866 - accuracy: 0.5440 - val_loss: 0.6825 - val_accuracy: 0.5639\n",
      "Epoch 25/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6837 - accuracy: 0.5572 - val_loss: 0.6819 - val_accuracy: 0.5554\n",
      "Epoch 26/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6873 - accuracy: 0.5557 - val_loss: 0.6813 - val_accuracy: 0.5568\n",
      "Epoch 27/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6818 - accuracy: 0.5704 - val_loss: 0.6804 - val_accuracy: 0.5767\n",
      "Epoch 28/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6823 - accuracy: 0.5792 - val_loss: 0.6818 - val_accuracy: 0.5568\n",
      "Epoch 29/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6829 - accuracy: 0.5806 - val_loss: 0.6790 - val_accuracy: 0.5838\n",
      "Epoch 30/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6791 - accuracy: 0.5733 - val_loss: 0.6794 - val_accuracy: 0.5312\n",
      "Epoch 31/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6802 - accuracy: 0.5748 - val_loss: 0.6775 - val_accuracy: 0.6463\n",
      "Epoch 32/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6803 - accuracy: 0.5748 - val_loss: 0.6763 - val_accuracy: 0.6250\n",
      "Epoch 33/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6745 - accuracy: 0.6202 - val_loss: 0.6774 - val_accuracy: 0.5895\n",
      "Epoch 34/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6762 - accuracy: 0.5938 - val_loss: 0.6760 - val_accuracy: 0.5469\n",
      "Epoch 35/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6699 - accuracy: 0.6158 - val_loss: 0.6789 - val_accuracy: 0.5526\n",
      "Epoch 36/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6733 - accuracy: 0.5806 - val_loss: 0.6724 - val_accuracy: 0.5639\n",
      "Epoch 37/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6731 - accuracy: 0.5865 - val_loss: 0.6756 - val_accuracy: 0.5341\n",
      "Epoch 38/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6765 - accuracy: 0.5762 - val_loss: 0.6702 - val_accuracy: 0.6520\n",
      "Epoch 39/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6749 - accuracy: 0.5850 - val_loss: 0.6754 - val_accuracy: 0.5298\n",
      "Epoch 40/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6758 - accuracy: 0.5836 - val_loss: 0.6696 - val_accuracy: 0.5724\n",
      "Epoch 41/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6698 - accuracy: 0.5865 - val_loss: 0.6888 - val_accuracy: 0.5071\n",
      "Epoch 42/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6776 - accuracy: 0.5411 - val_loss: 0.6686 - val_accuracy: 0.5739\n",
      "Epoch 43/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6664 - accuracy: 0.6023 - val_loss: 0.6710 - val_accuracy: 0.5483\n",
      "Epoch 44/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6700 - accuracy: 0.5880 - val_loss: 0.6663 - val_accuracy: 0.6406\n",
      "Epoch 45/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6632 - accuracy: 0.6056 - val_loss: 0.6692 - val_accuracy: 0.6065\n",
      "Epoch 46/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6744 - accuracy: 0.5696 - val_loss: 0.6647 - val_accuracy: 0.6406\n",
      "Epoch 47/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6698 - accuracy: 0.6041 - val_loss: 0.6643 - val_accuracy: 0.6449\n",
      "Epoch 48/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6651 - accuracy: 0.6012 - val_loss: 0.6743 - val_accuracy: 0.5554\n",
      "Epoch 49/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6690 - accuracy: 0.5894 - val_loss: 0.6668 - val_accuracy: 0.6023\n",
      "Epoch 50/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6670 - accuracy: 0.5865 - val_loss: 0.6663 - val_accuracy: 0.5554\n",
      "Epoch 51/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6630 - accuracy: 0.5968 - val_loss: 0.6639 - val_accuracy: 0.6222\n",
      "Epoch 52/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6726 - accuracy: 0.5733 - val_loss: 0.6602 - val_accuracy: 0.6548\n",
      "Epoch 53/300\n",
      "11/11 [==============================] - 20s 2s/step - loss: 0.6622 - accuracy: 0.5968 - val_loss: 0.6574 - val_accuracy: 0.6562\n",
      "Epoch 54/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6580 - accuracy: 0.6276 - val_loss: 0.6592 - val_accuracy: 0.6477\n",
      "Epoch 55/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6677 - accuracy: 0.5909 - val_loss: 0.6576 - val_accuracy: 0.6236\n",
      "Epoch 56/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6591 - accuracy: 0.5938 - val_loss: 0.6581 - val_accuracy: 0.5994\n",
      "Epoch 57/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6548 - accuracy: 0.6085 - val_loss: 0.6720 - val_accuracy: 0.5568\n",
      "Epoch 58/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6455 - accuracy: 0.6261 - val_loss: 0.6655 - val_accuracy: 0.5639\n",
      "Epoch 59/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6535 - accuracy: 0.6100 - val_loss: 0.6578 - val_accuracy: 0.5938\n",
      "Epoch 60/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6479 - accuracy: 0.6158 - val_loss: 0.6668 - val_accuracy: 0.5582\n",
      "Epoch 61/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6538 - accuracy: 0.6222 - val_loss: 0.6604 - val_accuracy: 0.5781\n",
      "Epoch 62/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6490 - accuracy: 0.6217 - val_loss: 0.6638 - val_accuracy: 0.5668\n",
      "Epoch 63/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6568 - accuracy: 0.6290 - val_loss: 0.6728 - val_accuracy: 0.5582\n",
      "Epoch 64/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6660 - accuracy: 0.6085 - val_loss: 0.6586 - val_accuracy: 0.5753\n",
      "Epoch 65/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6563 - accuracy: 0.6305 - val_loss: 0.6483 - val_accuracy: 0.6605\n",
      "Epoch 66/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6503 - accuracy: 0.6129 - val_loss: 0.6527 - val_accuracy: 0.6349\n",
      "Epoch 67/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6472 - accuracy: 0.6334 - val_loss: 0.6482 - val_accuracy: 0.6577\n",
      "Epoch 68/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6543 - accuracy: 0.6466 - val_loss: 0.6484 - val_accuracy: 0.6293\n",
      "Epoch 69/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6559 - accuracy: 0.6144 - val_loss: 0.6574 - val_accuracy: 0.5824\n",
      "Epoch 70/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6480 - accuracy: 0.6378 - val_loss: 0.6479 - val_accuracy: 0.6278\n",
      "Epoch 71/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6517 - accuracy: 0.6334 - val_loss: 0.6446 - val_accuracy: 0.6591\n",
      "Epoch 72/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6464 - accuracy: 0.6452 - val_loss: 0.6612 - val_accuracy: 0.5909\n",
      "Epoch 73/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6622 - accuracy: 0.6085 - val_loss: 0.6435 - val_accuracy: 0.6676\n",
      "Epoch 74/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6475 - accuracy: 0.6261 - val_loss: 0.6486 - val_accuracy: 0.6420\n",
      "Epoch 75/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6537 - accuracy: 0.6320 - val_loss: 0.6431 - val_accuracy: 0.6676\n",
      "Epoch 76/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6564 - accuracy: 0.6261 - val_loss: 0.6423 - val_accuracy: 0.6619\n",
      "Epoch 77/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6435 - accuracy: 0.6261 - val_loss: 0.6426 - val_accuracy: 0.6420\n",
      "Epoch 78/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6561 - accuracy: 0.6202 - val_loss: 0.6551 - val_accuracy: 0.5838\n",
      "Epoch 79/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6546 - accuracy: 0.6246 - val_loss: 0.6403 - val_accuracy: 0.6577\n",
      "Epoch 80/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6425 - accuracy: 0.6525 - val_loss: 0.6523 - val_accuracy: 0.6023\n",
      "Epoch 81/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6461 - accuracy: 0.6290 - val_loss: 0.6409 - val_accuracy: 0.6548\n",
      "Epoch 82/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6497 - accuracy: 0.6188 - val_loss: 0.6599 - val_accuracy: 0.5881\n",
      "Epoch 83/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6554 - accuracy: 0.6173 - val_loss: 0.6368 - val_accuracy: 0.6705\n",
      "Epoch 84/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6485 - accuracy: 0.6378 - val_loss: 0.6495 - val_accuracy: 0.6023\n",
      "Epoch 85/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6503 - accuracy: 0.6100 - val_loss: 0.6397 - val_accuracy: 0.6719\n",
      "Epoch 86/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6443 - accuracy: 0.6305 - val_loss: 0.6344 - val_accuracy: 0.6733\n",
      "Epoch 87/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6432 - accuracy: 0.6437 - val_loss: 0.6542 - val_accuracy: 0.6009\n",
      "Epoch 88/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6479 - accuracy: 0.6525 - val_loss: 0.6350 - val_accuracy: 0.6733\n",
      "Epoch 89/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6461 - accuracy: 0.6378 - val_loss: 0.6383 - val_accuracy: 0.6506\n",
      "Epoch 90/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6477 - accuracy: 0.6349 - val_loss: 0.6350 - val_accuracy: 0.6776\n",
      "Epoch 91/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6424 - accuracy: 0.6349 - val_loss: 0.6375 - val_accuracy: 0.6548\n",
      "Epoch 92/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6450 - accuracy: 0.6232 - val_loss: 0.6361 - val_accuracy: 0.6605\n",
      "Epoch 93/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6408 - accuracy: 0.6364 - val_loss: 0.6406 - val_accuracy: 0.6264\n",
      "Epoch 94/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6558 - accuracy: 0.6202 - val_loss: 0.6329 - val_accuracy: 0.6676\n",
      "Epoch 95/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6271 - accuracy: 0.6540 - val_loss: 0.6476 - val_accuracy: 0.5994\n",
      "Epoch 96/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6548 - accuracy: 0.6129 - val_loss: 0.6328 - val_accuracy: 0.6719\n",
      "Epoch 97/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6409 - accuracy: 0.6393 - val_loss: 0.6328 - val_accuracy: 0.6733\n",
      "Epoch 98/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6432 - accuracy: 0.6293 - val_loss: 0.6314 - val_accuracy: 0.6690\n",
      "Epoch 99/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6405 - accuracy: 0.6290 - val_loss: 0.6452 - val_accuracy: 0.6065\n",
      "Epoch 100/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6349 - accuracy: 0.6364 - val_loss: 0.6287 - val_accuracy: 0.6733\n",
      "Epoch 101/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6461 - accuracy: 0.6261 - val_loss: 0.6286 - val_accuracy: 0.6705\n",
      "Epoch 102/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6416 - accuracy: 0.6406 - val_loss: 0.6263 - val_accuracy: 0.6690\n",
      "Epoch 103/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6474 - accuracy: 0.5938 - val_loss: 0.6316 - val_accuracy: 0.6463\n",
      "Epoch 104/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6461 - accuracy: 0.6496 - val_loss: 0.6278 - val_accuracy: 0.6733\n",
      "Epoch 105/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6340 - accuracy: 0.6496 - val_loss: 0.6317 - val_accuracy: 0.6506\n",
      "Epoch 106/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6483 - accuracy: 0.6144 - val_loss: 0.6386 - val_accuracy: 0.6193\n",
      "Epoch 107/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6378 - accuracy: 0.6452 - val_loss: 0.6420 - val_accuracy: 0.6165\n",
      "Epoch 108/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6386 - accuracy: 0.6334 - val_loss: 0.6313 - val_accuracy: 0.6562\n",
      "Epoch 109/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6440 - accuracy: 0.6510 - val_loss: 0.6279 - val_accuracy: 0.6591\n",
      "Epoch 110/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6298 - accuracy: 0.6584 - val_loss: 0.6343 - val_accuracy: 0.6293\n",
      "Epoch 111/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6346 - accuracy: 0.6232 - val_loss: 0.6343 - val_accuracy: 0.6278\n",
      "Epoch 112/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6412 - accuracy: 0.6320 - val_loss: 0.6344 - val_accuracy: 0.6293\n",
      "Epoch 113/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6295 - accuracy: 0.6598 - val_loss: 0.6539 - val_accuracy: 0.6037\n",
      "Epoch 114/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6516 - accuracy: 0.6364 - val_loss: 0.6222 - val_accuracy: 0.6747\n",
      "Epoch 115/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6460 - accuracy: 0.6276 - val_loss: 0.6211 - val_accuracy: 0.6832\n",
      "Epoch 116/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6310 - accuracy: 0.6760 - val_loss: 0.6298 - val_accuracy: 0.6335\n",
      "Epoch 117/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6320 - accuracy: 0.6628 - val_loss: 0.6278 - val_accuracy: 0.6562\n",
      "Epoch 118/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6317 - accuracy: 0.6496 - val_loss: 0.6234 - val_accuracy: 0.6733\n",
      "Epoch 119/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6400 - accuracy: 0.6349 - val_loss: 0.6192 - val_accuracy: 0.6761\n",
      "Epoch 120/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6335 - accuracy: 0.6554 - val_loss: 0.6365 - val_accuracy: 0.6207\n",
      "Epoch 121/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6489 - accuracy: 0.6452 - val_loss: 0.6292 - val_accuracy: 0.6378\n",
      "Epoch 122/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6288 - accuracy: 0.6408 - val_loss: 0.6230 - val_accuracy: 0.6435\n",
      "Epoch 123/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6374 - accuracy: 0.6320 - val_loss: 0.6161 - val_accuracy: 0.6790\n",
      "Epoch 124/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6264 - accuracy: 0.6491 - val_loss: 0.6148 - val_accuracy: 0.6776\n",
      "Epoch 125/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6340 - accuracy: 0.6525 - val_loss: 0.6230 - val_accuracy: 0.6534\n",
      "Epoch 126/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6343 - accuracy: 0.6452 - val_loss: 0.6242 - val_accuracy: 0.6577\n",
      "Epoch 127/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6364 - accuracy: 0.6246 - val_loss: 0.6250 - val_accuracy: 0.6477\n",
      "Epoch 128/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6266 - accuracy: 0.6584 - val_loss: 0.6136 - val_accuracy: 0.6832\n",
      "Epoch 129/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6345 - accuracy: 0.6364 - val_loss: 0.6135 - val_accuracy: 0.6818\n",
      "Epoch 130/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6219 - accuracy: 0.6657 - val_loss: 0.6263 - val_accuracy: 0.6364\n",
      "Epoch 131/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6404 - accuracy: 0.6290 - val_loss: 0.6137 - val_accuracy: 0.6719\n",
      "Epoch 132/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6373 - accuracy: 0.6437 - val_loss: 0.6097 - val_accuracy: 0.6889\n",
      "Epoch 133/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6367 - accuracy: 0.6422 - val_loss: 0.6176 - val_accuracy: 0.6449\n",
      "Epoch 134/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6312 - accuracy: 0.6422 - val_loss: 0.6102 - val_accuracy: 0.6719\n",
      "Epoch 135/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6273 - accuracy: 0.6393 - val_loss: 0.6074 - val_accuracy: 0.6875\n",
      "Epoch 136/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6272 - accuracy: 0.6525 - val_loss: 0.6103 - val_accuracy: 0.6690\n",
      "Epoch 137/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6251 - accuracy: 0.6481 - val_loss: 0.6140 - val_accuracy: 0.6548\n",
      "Epoch 138/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6378 - accuracy: 0.6422 - val_loss: 0.6116 - val_accuracy: 0.6733\n",
      "Epoch 139/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6266 - accuracy: 0.6672 - val_loss: 0.6283 - val_accuracy: 0.6278\n",
      "Epoch 140/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6352 - accuracy: 0.6246 - val_loss: 0.6044 - val_accuracy: 0.6903\n",
      "Epoch 141/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6323 - accuracy: 0.6540 - val_loss: 0.6099 - val_accuracy: 0.6776\n",
      "Epoch 142/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6179 - accuracy: 0.6598 - val_loss: 0.6353 - val_accuracy: 0.6321\n",
      "Epoch 143/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6185 - accuracy: 0.6466 - val_loss: 0.6124 - val_accuracy: 0.6634\n",
      "Epoch 144/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6340 - accuracy: 0.6554 - val_loss: 0.5981 - val_accuracy: 0.6861\n",
      "Epoch 145/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6203 - accuracy: 0.6548 - val_loss: 0.6032 - val_accuracy: 0.6690\n",
      "Epoch 146/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6270 - accuracy: 0.6657 - val_loss: 0.5953 - val_accuracy: 0.7003\n",
      "Epoch 147/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6221 - accuracy: 0.6422 - val_loss: 0.5965 - val_accuracy: 0.6776\n",
      "Epoch 148/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6123 - accuracy: 0.6833 - val_loss: 0.6150 - val_accuracy: 0.6520\n",
      "Epoch 149/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6286 - accuracy: 0.6466 - val_loss: 0.5965 - val_accuracy: 0.6804\n",
      "Epoch 150/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6276 - accuracy: 0.6496 - val_loss: 0.6006 - val_accuracy: 0.6776\n",
      "Epoch 151/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6284 - accuracy: 0.6525 - val_loss: 0.5971 - val_accuracy: 0.6761\n",
      "Epoch 152/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6250 - accuracy: 0.6437 - val_loss: 0.5990 - val_accuracy: 0.6719\n",
      "Epoch 153/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6370 - accuracy: 0.6144 - val_loss: 0.5982 - val_accuracy: 0.6790\n",
      "Epoch 154/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6148 - accuracy: 0.6672 - val_loss: 0.5951 - val_accuracy: 0.6861\n",
      "Epoch 155/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6373 - accuracy: 0.6496 - val_loss: 0.5979 - val_accuracy: 0.6690\n",
      "Epoch 156/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6242 - accuracy: 0.6584 - val_loss: 0.5924 - val_accuracy: 0.6918\n",
      "Epoch 157/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6320 - accuracy: 0.6305 - val_loss: 0.6031 - val_accuracy: 0.6662\n",
      "Epoch 158/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6279 - accuracy: 0.6307 - val_loss: 0.5920 - val_accuracy: 0.7017\n",
      "Epoch 159/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6321 - accuracy: 0.6613 - val_loss: 0.5929 - val_accuracy: 0.7031\n",
      "Epoch 160/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6301 - accuracy: 0.6584 - val_loss: 0.5912 - val_accuracy: 0.6932\n",
      "Epoch 161/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6274 - accuracy: 0.6378 - val_loss: 0.6041 - val_accuracy: 0.6463\n",
      "Epoch 162/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6223 - accuracy: 0.6261 - val_loss: 0.5956 - val_accuracy: 0.7003\n",
      "Epoch 163/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6279 - accuracy: 0.6540 - val_loss: 0.5991 - val_accuracy: 0.6648\n",
      "Epoch 164/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6261 - accuracy: 0.6422 - val_loss: 0.5892 - val_accuracy: 0.6918\n",
      "Epoch 165/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6193 - accuracy: 0.6334 - val_loss: 0.5985 - val_accuracy: 0.6619\n",
      "Epoch 166/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6281 - accuracy: 0.6481 - val_loss: 0.5935 - val_accuracy: 0.6818\n",
      "Epoch 167/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6173 - accuracy: 0.6540 - val_loss: 0.5954 - val_accuracy: 0.6747\n",
      "Epoch 168/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6297 - accuracy: 0.6378 - val_loss: 0.5978 - val_accuracy: 0.6761\n",
      "Epoch 169/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6224 - accuracy: 0.6584 - val_loss: 0.5918 - val_accuracy: 0.6832\n",
      "Epoch 170/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6184 - accuracy: 0.6408 - val_loss: 0.5905 - val_accuracy: 0.6818\n",
      "Epoch 171/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6170 - accuracy: 0.6657 - val_loss: 0.5907 - val_accuracy: 0.7031\n",
      "Epoch 172/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6246 - accuracy: 0.6349 - val_loss: 0.5875 - val_accuracy: 0.6989\n",
      "Epoch 173/300\n",
      "11/11 [==============================] - 20s 2s/step - loss: 0.6089 - accuracy: 0.6642 - val_loss: 0.6056 - val_accuracy: 0.6761\n",
      "Epoch 174/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6181 - accuracy: 0.6657 - val_loss: 0.6114 - val_accuracy: 0.6406\n",
      "Epoch 175/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6076 - accuracy: 0.6657 - val_loss: 0.5859 - val_accuracy: 0.6960\n",
      "Epoch 176/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6101 - accuracy: 0.6642 - val_loss: 0.5830 - val_accuracy: 0.7031\n",
      "Epoch 177/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6280 - accuracy: 0.6420 - val_loss: 0.6135 - val_accuracy: 0.6378\n",
      "Epoch 178/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6175 - accuracy: 0.6481 - val_loss: 0.5851 - val_accuracy: 0.6918\n",
      "Epoch 179/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6311 - accuracy: 0.6452 - val_loss: 0.5828 - val_accuracy: 0.6932\n",
      "Epoch 180/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6228 - accuracy: 0.6598 - val_loss: 0.5904 - val_accuracy: 0.6804\n",
      "Epoch 181/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6072 - accuracy: 0.6774 - val_loss: 0.6088 - val_accuracy: 0.6463\n",
      "Epoch 182/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6186 - accuracy: 0.6525 - val_loss: 0.6100 - val_accuracy: 0.6605\n",
      "Epoch 183/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6146 - accuracy: 0.6540 - val_loss: 0.5853 - val_accuracy: 0.6776\n",
      "Epoch 184/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6218 - accuracy: 0.6584 - val_loss: 0.5865 - val_accuracy: 0.6761\n",
      "Epoch 185/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6004 - accuracy: 0.6716 - val_loss: 0.5801 - val_accuracy: 0.6989\n",
      "Epoch 186/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6317 - accuracy: 0.6364 - val_loss: 0.5772 - val_accuracy: 0.6960\n",
      "Epoch 187/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6088 - accuracy: 0.6657 - val_loss: 0.5846 - val_accuracy: 0.6719\n",
      "Epoch 188/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6226 - accuracy: 0.6510 - val_loss: 0.5895 - val_accuracy: 0.6832\n",
      "Epoch 189/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6304 - accuracy: 0.6305 - val_loss: 0.5809 - val_accuracy: 0.6974\n",
      "Epoch 190/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6093 - accuracy: 0.6716 - val_loss: 0.6031 - val_accuracy: 0.6690\n",
      "Epoch 191/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6110 - accuracy: 0.6745 - val_loss: 0.5964 - val_accuracy: 0.6463\n",
      "Epoch 192/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6080 - accuracy: 0.6584 - val_loss: 0.6007 - val_accuracy: 0.6435\n",
      "Epoch 193/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6243 - accuracy: 0.6745 - val_loss: 0.5735 - val_accuracy: 0.7116\n",
      "Epoch 194/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6221 - accuracy: 0.6554 - val_loss: 0.5733 - val_accuracy: 0.6974\n",
      "Epoch 195/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6107 - accuracy: 0.6584 - val_loss: 0.5794 - val_accuracy: 0.6889\n",
      "Epoch 196/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6217 - accuracy: 0.6569 - val_loss: 0.5701 - val_accuracy: 0.7045\n",
      "Epoch 197/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6221 - accuracy: 0.6730 - val_loss: 0.5772 - val_accuracy: 0.7045\n",
      "Epoch 198/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6169 - accuracy: 0.6481 - val_loss: 0.6310 - val_accuracy: 0.6250\n",
      "Epoch 199/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6224 - accuracy: 0.6554 - val_loss: 0.5733 - val_accuracy: 0.7102\n",
      "Epoch 200/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6191 - accuracy: 0.6496 - val_loss: 0.6019 - val_accuracy: 0.6392\n",
      "Epoch 201/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6231 - accuracy: 0.6510 - val_loss: 0.5885 - val_accuracy: 0.6562\n",
      "Epoch 202/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6129 - accuracy: 0.6613 - val_loss: 0.6057 - val_accuracy: 0.6420\n",
      "Epoch 203/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6139 - accuracy: 0.6584 - val_loss: 0.5682 - val_accuracy: 0.7060\n",
      "Epoch 204/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6078 - accuracy: 0.6719 - val_loss: 0.5681 - val_accuracy: 0.7116\n",
      "Epoch 205/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6265 - accuracy: 0.6569 - val_loss: 0.5852 - val_accuracy: 0.6577\n",
      "Epoch 206/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6095 - accuracy: 0.6672 - val_loss: 0.5720 - val_accuracy: 0.7060\n",
      "Epoch 207/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6115 - accuracy: 0.6686 - val_loss: 0.6018 - val_accuracy: 0.6506\n",
      "Epoch 208/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6143 - accuracy: 0.6613 - val_loss: 0.5702 - val_accuracy: 0.7003\n",
      "Epoch 209/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6166 - accuracy: 0.6408 - val_loss: 0.5735 - val_accuracy: 0.6889\n",
      "Epoch 210/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6090 - accuracy: 0.6554 - val_loss: 0.6026 - val_accuracy: 0.6562\n",
      "Epoch 211/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6259 - accuracy: 0.6510 - val_loss: 0.5690 - val_accuracy: 0.7102\n",
      "Epoch 212/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.5977 - accuracy: 0.6877 - val_loss: 0.5691 - val_accuracy: 0.7003\n",
      "Epoch 213/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6136 - accuracy: 0.6525 - val_loss: 0.5862 - val_accuracy: 0.6619\n",
      "Epoch 214/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6115 - accuracy: 0.6598 - val_loss: 0.5661 - val_accuracy: 0.7131\n",
      "Epoch 215/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.5928 - accuracy: 0.6906 - val_loss: 0.5918 - val_accuracy: 0.6847\n",
      "Epoch 216/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6065 - accuracy: 0.6774 - val_loss: 0.5758 - val_accuracy: 0.6832\n",
      "Epoch 217/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6097 - accuracy: 0.6760 - val_loss: 0.5820 - val_accuracy: 0.6634\n",
      "Epoch 218/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6089 - accuracy: 0.6642 - val_loss: 0.5721 - val_accuracy: 0.6875\n",
      "Epoch 219/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6043 - accuracy: 0.6686 - val_loss: 0.5968 - val_accuracy: 0.6705\n",
      "Epoch 220/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6216 - accuracy: 0.6598 - val_loss: 0.5722 - val_accuracy: 0.6889\n",
      "Epoch 221/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6057 - accuracy: 0.6716 - val_loss: 0.5731 - val_accuracy: 0.6918\n",
      "Epoch 222/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6253 - accuracy: 0.6554 - val_loss: 0.5681 - val_accuracy: 0.7102\n",
      "Epoch 223/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6093 - accuracy: 0.6584 - val_loss: 0.5801 - val_accuracy: 0.6634\n",
      "Epoch 224/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6206 - accuracy: 0.6525 - val_loss: 0.5648 - val_accuracy: 0.7173\n",
      "Epoch 225/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6085 - accuracy: 0.6613 - val_loss: 0.5672 - val_accuracy: 0.6847\n",
      "Epoch 226/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6245 - accuracy: 0.6598 - val_loss: 0.5974 - val_accuracy: 0.6534\n",
      "Epoch 227/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6132 - accuracy: 0.6569 - val_loss: 0.5624 - val_accuracy: 0.7045\n",
      "Epoch 228/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6116 - accuracy: 0.6481 - val_loss: 0.5705 - val_accuracy: 0.6875\n",
      "Epoch 229/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6049 - accuracy: 0.6745 - val_loss: 0.5844 - val_accuracy: 0.6619\n",
      "Epoch 230/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6177 - accuracy: 0.6760 - val_loss: 0.5588 - val_accuracy: 0.7131\n",
      "Epoch 231/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6023 - accuracy: 0.6584 - val_loss: 0.5555 - val_accuracy: 0.7131\n",
      "Epoch 232/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.5975 - accuracy: 0.6790 - val_loss: 0.6271 - val_accuracy: 0.6321\n",
      "Epoch 233/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6165 - accuracy: 0.6496 - val_loss: 0.5555 - val_accuracy: 0.7088\n",
      "Epoch 234/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6182 - accuracy: 0.6701 - val_loss: 0.5890 - val_accuracy: 0.6591\n",
      "Epoch 235/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6077 - accuracy: 0.6584 - val_loss: 0.5544 - val_accuracy: 0.7116\n",
      "Epoch 236/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6092 - accuracy: 0.6648 - val_loss: 0.5761 - val_accuracy: 0.7045\n",
      "Epoch 237/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6232 - accuracy: 0.6276 - val_loss: 0.6036 - val_accuracy: 0.6506\n",
      "Epoch 238/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6075 - accuracy: 0.6760 - val_loss: 0.5585 - val_accuracy: 0.7060\n",
      "Epoch 239/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6309 - accuracy: 0.6481 - val_loss: 0.5564 - val_accuracy: 0.7216\n",
      "Epoch 240/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6096 - accuracy: 0.6804 - val_loss: 0.5665 - val_accuracy: 0.6903\n",
      "Epoch 241/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6051 - accuracy: 0.6628 - val_loss: 0.5527 - val_accuracy: 0.7173\n",
      "Epoch 242/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6089 - accuracy: 0.6672 - val_loss: 0.5527 - val_accuracy: 0.7173\n",
      "Epoch 243/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6031 - accuracy: 0.6818 - val_loss: 0.5592 - val_accuracy: 0.7017\n",
      "Epoch 244/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6077 - accuracy: 0.6818 - val_loss: 0.5628 - val_accuracy: 0.7188\n",
      "Epoch 245/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6046 - accuracy: 0.6745 - val_loss: 0.5554 - val_accuracy: 0.7287\n",
      "Epoch 246/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6126 - accuracy: 0.6862 - val_loss: 0.5576 - val_accuracy: 0.7088\n",
      "Epoch 247/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6081 - accuracy: 0.6804 - val_loss: 0.5817 - val_accuracy: 0.6676\n",
      "Epoch 248/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.5931 - accuracy: 0.6804 - val_loss: 0.5713 - val_accuracy: 0.7003\n",
      "Epoch 249/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6026 - accuracy: 0.6657 - val_loss: 0.5601 - val_accuracy: 0.7173\n",
      "Epoch 250/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6240 - accuracy: 0.6422 - val_loss: 0.5610 - val_accuracy: 0.7159\n",
      "Epoch 251/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6015 - accuracy: 0.6891 - val_loss: 0.5660 - val_accuracy: 0.6903\n",
      "Epoch 252/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.5976 - accuracy: 0.6716 - val_loss: 0.5937 - val_accuracy: 0.6562\n",
      "Epoch 253/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6055 - accuracy: 0.6789 - val_loss: 0.5858 - val_accuracy: 0.6548\n",
      "Epoch 254/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.5997 - accuracy: 0.6686 - val_loss: 0.5550 - val_accuracy: 0.7102\n",
      "Epoch 255/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6045 - accuracy: 0.6554 - val_loss: 0.5567 - val_accuracy: 0.7102\n",
      "Epoch 256/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.5985 - accuracy: 0.6642 - val_loss: 0.5697 - val_accuracy: 0.6747\n",
      "Epoch 257/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.5912 - accuracy: 0.6716 - val_loss: 0.5767 - val_accuracy: 0.6705\n",
      "Epoch 258/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6012 - accuracy: 0.6747 - val_loss: 0.5822 - val_accuracy: 0.6506\n",
      "Epoch 259/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.5976 - accuracy: 0.6833 - val_loss: 0.5518 - val_accuracy: 0.7131\n",
      "Epoch 260/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6146 - accuracy: 0.6733 - val_loss: 0.5725 - val_accuracy: 0.6648\n",
      "Epoch 261/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.5986 - accuracy: 0.6730 - val_loss: 0.5477 - val_accuracy: 0.7301\n",
      "Epoch 262/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.5938 - accuracy: 0.6686 - val_loss: 0.5530 - val_accuracy: 0.7003\n",
      "Epoch 263/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6055 - accuracy: 0.6716 - val_loss: 0.5506 - val_accuracy: 0.7159\n",
      "Epoch 264/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6081 - accuracy: 0.6613 - val_loss: 0.5694 - val_accuracy: 0.6790\n",
      "Epoch 265/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.5894 - accuracy: 0.6716 - val_loss: 0.5451 - val_accuracy: 0.7315\n",
      "Epoch 266/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6002 - accuracy: 0.6686 - val_loss: 0.5675 - val_accuracy: 0.6804\n",
      "Epoch 267/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6190 - accuracy: 0.6642 - val_loss: 0.5405 - val_accuracy: 0.7358\n",
      "Epoch 268/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6121 - accuracy: 0.6569 - val_loss: 0.5468 - val_accuracy: 0.7188\n",
      "Epoch 269/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6170 - accuracy: 0.6452 - val_loss: 0.5656 - val_accuracy: 0.6861\n",
      "Epoch 270/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6017 - accuracy: 0.6657 - val_loss: 0.5702 - val_accuracy: 0.6818\n",
      "Epoch 271/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.5985 - accuracy: 0.6613 - val_loss: 0.5635 - val_accuracy: 0.6974\n",
      "Epoch 272/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.5924 - accuracy: 0.6804 - val_loss: 0.5475 - val_accuracy: 0.7131\n",
      "Epoch 273/300\n",
      "11/11 [==============================] - 357s 32s/step - loss: 0.5940 - accuracy: 0.6877 - val_loss: 0.5941 - val_accuracy: 0.6477\n",
      "Epoch 274/300\n",
      "11/11 [==============================] - 69s 6s/step - loss: 0.6161 - accuracy: 0.6540 - val_loss: 0.5534 - val_accuracy: 0.6932\n",
      "Epoch 275/300\n",
      "11/11 [==============================] - 20s 2s/step - loss: 0.6017 - accuracy: 0.6833 - val_loss: 0.6057 - val_accuracy: 0.6406\n",
      "Epoch 276/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6035 - accuracy: 0.6642 - val_loss: 0.5464 - val_accuracy: 0.7159\n",
      "Epoch 277/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.5893 - accuracy: 0.6862 - val_loss: 0.5404 - val_accuracy: 0.7259\n",
      "Epoch 278/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.5945 - accuracy: 0.6496 - val_loss: 0.5778 - val_accuracy: 0.6591\n",
      "Epoch 279/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6103 - accuracy: 0.6613 - val_loss: 0.6137 - val_accuracy: 0.6307\n",
      "Epoch 280/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.5937 - accuracy: 0.6862 - val_loss: 0.5354 - val_accuracy: 0.7500\n",
      "Epoch 281/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.5878 - accuracy: 0.6833 - val_loss: 0.5305 - val_accuracy: 0.7358\n",
      "Epoch 282/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6167 - accuracy: 0.6422 - val_loss: 0.5362 - val_accuracy: 0.7259\n",
      "Epoch 283/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6042 - accuracy: 0.6554 - val_loss: 0.5912 - val_accuracy: 0.6506\n",
      "Epoch 284/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6121 - accuracy: 0.6540 - val_loss: 0.5446 - val_accuracy: 0.7145\n",
      "Epoch 285/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6005 - accuracy: 0.6789 - val_loss: 0.5383 - val_accuracy: 0.7216\n",
      "Epoch 286/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.5773 - accuracy: 0.7097 - val_loss: 0.5336 - val_accuracy: 0.7330\n",
      "Epoch 287/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.5956 - accuracy: 0.6848 - val_loss: 0.5496 - val_accuracy: 0.7102\n",
      "Epoch 288/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6123 - accuracy: 0.6672 - val_loss: 0.5382 - val_accuracy: 0.7358\n",
      "Epoch 289/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.5840 - accuracy: 0.6818 - val_loss: 0.5947 - val_accuracy: 0.6463\n",
      "Epoch 290/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.5783 - accuracy: 0.7053 - val_loss: 0.5280 - val_accuracy: 0.7315\n",
      "Epoch 291/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6081 - accuracy: 0.6686 - val_loss: 0.5434 - val_accuracy: 0.7145\n",
      "Epoch 292/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.5959 - accuracy: 0.6672 - val_loss: 0.5978 - val_accuracy: 0.6449\n",
      "Epoch 293/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6021 - accuracy: 0.6833 - val_loss: 0.5498 - val_accuracy: 0.7088\n",
      "Epoch 294/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.6017 - accuracy: 0.6628 - val_loss: 0.5324 - val_accuracy: 0.7401\n",
      "Epoch 295/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.5845 - accuracy: 0.6862 - val_loss: 0.5296 - val_accuracy: 0.7372\n",
      "Epoch 296/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.5657 - accuracy: 0.6877 - val_loss: 0.5944 - val_accuracy: 0.6648\n",
      "Epoch 297/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.5857 - accuracy: 0.6906 - val_loss: 0.5377 - val_accuracy: 0.7358\n",
      "Epoch 298/300\n",
      "11/11 [==============================] - 19s 2s/step - loss: 0.5942 - accuracy: 0.6965 - val_loss: 0.5315 - val_accuracy: 0.7401\n",
      "Epoch 299/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6117 - accuracy: 0.6525 - val_loss: 0.5337 - val_accuracy: 0.7315\n",
      "Epoch 300/300\n",
      "11/11 [==============================] - 18s 2s/step - loss: 0.6005 - accuracy: 0.6730 - val_loss: 0.5316 - val_accuracy: 0.7429\n"
     ]
    }
   ],
   "source": [
    "model.compile(loss=\"sparse_categorical_crossentropy\",\n",
    "              optimizer=\"sgd\",\n",
    "              metrics = [\"accuracy\"])\n",
    "epochs = 300\n",
    "history = model.fit_generator(train_generator,\n",
    "                              steps_per_epoch=train_num // batch_size,\n",
    "                              epochs=epochs,\n",
    "                              validation_data=validation_generator,\n",
    "                              validation_steps=valid_num // batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_learing_curves(history,label,epochs,min_value,max_value):\n",
    "    data={}\n",
    "    data[label] = history.history[label]\n",
    "    data['val'+label] = history.history['val_'+label]\n",
    "    pd.DataFrame(data).plot(figsize=(8,5))\n",
    "    plt.grid(True)\n",
    "    plt.axis([0,epochs,min_value,max_value])\n",
    "    plt.show()\n",
    "\n",
    "plot_learing_curves(history,'accuracy',epochs,0,1)\n",
    "plot_learing_curves(history,'loss',epochs,0,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}